{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c9e2048",
   "metadata": {},
   "source": [
    "# Fine-Tuning ChatGPT for Different Data Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e8ddc61",
   "metadata": {},
   "source": [
    "# Imports\n",
    "Here we import all the necessary packages and modules for our fine-tuning process.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b410a579",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import pprint\n",
    "import time\n",
    "import argparse\n",
    "from ast import literal_eval\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tqdm import tqdm\n",
    "\n",
    "import wandb\n",
    "from openai import OpenAI\n",
    "import pandas as pd\n",
    "\n",
    "from utils_2 import (\n",
    "    dataset_has_format_errors,\n",
    "    check_token_statistics_and_cost_estimate,\n",
    "    load_dataset_task_prompt_mappings,\n",
    "    write_jsonl,\n",
    ")\n",
    "from utils_1 import task_num_to_task_name, dataset_num_to_dataset_name, plot_count_and_normalized_confusion_matrix, task_to_display_labels\n",
    "\n",
    "# Get the notebook's full path\n",
    "notebook_path = os.getcwd()\n",
    "\n",
    "# Set module_dir to the notebook's directory\n",
    "module_dir = notebook_path\n",
    "\n",
    "#api_key_project = \"\" #MK's Key\n",
    "api_key_project = \"\" #new PRODIGI key\n",
    "\n",
    "client = OpenAI(\n",
    "    api_key=api_key_project,  # This is the default and can be omitted\n",
    ")\n",
    "\n",
    "\n",
    "WANDB_PROJECT_NAME = \"chatgpt_annotations_llm_comparison\"\n",
    "MODEL_NAME = 'gpt-4o-mini-2024-07-18'\n",
    "COMPLETION_RETRIES = 50\n",
    "\n",
    "print(notebook_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772bcc76",
   "metadata": {},
   "source": [
    "# Utility Functions\n",
    "These functions provide various utilities for tasks such as data loading, token statistics, and other preprocessing steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa7e1cb4",
   "metadata": {},
   "source": [
    "## Configuration Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c79142f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuration Variables\n",
    "# Manually set the variables that were previously handled by argparse\n",
    "\n",
    "# Type of task to run inference on\n",
    "task = 1  # Choices: [1]\n",
    "\n",
    "# Dataset to run inference on\n",
    "dataset = 7  # Choices: [1, 2, 3, 4, 5, 6]\n",
    "\n",
    "# Expert Dataset for Second Evaluation\n",
    "dataset_eval = 'x' # Choises: ['x']\n",
    "\n",
    "# Size of the sample to generate\n",
    "sample_size = '100'  # Choices: ['100','200']\n",
    "\n",
    "# Path to the directory to store the generated samples\n",
    "output_dir = '../../annotations/chatGPT/output_fine_tuning/'\n",
    "\n",
    "# Random seed to use\n",
    "seed = 2019\n",
    "\n",
    "# Path to the directory containing the datasets\n",
    "data_dir = '../../annotations/chatGPT/input_fine_tuning/'\n",
    "\n",
    "# Whether to use the full label\n",
    "not_use_full_labels = False\n",
    "\n",
    "# Path to the dataset-task mappings file\n",
    "dataset_task_mappings_fp = os.path.normpath(os.path.join(module_dir, '../../task_mappings', 'dataset_task_mappings.csv'))\n",
    "\n",
    "# Whether to rewrite the dataframe in OpenAI format\n",
    "rewrite_df_in_openai = True\n",
    "\n",
    "# Number of epochs to train the model\n",
    "n_epochs = 5\n",
    "\n",
    "# Batch Size for learning\n",
    "n_batch = 20\n",
    "\n",
    "# Name of the run\n",
    "run_name = 'finetune_chetGPT_hatespeech_gpt_zero'\n",
    "\n",
    "# Temperature to use when generating text\n",
    "temp = 0.0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e81ba8",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ef6fa17",
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_label_to_completion(label: str, task_num: int, full_label: bool = True) -> str:\n",
    "    new_label = ''\n",
    "\n",
    "    if task_num == 1:\n",
    "        if full_label:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'HATE SPEECH'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'TOXIC SPEECH'\n",
    "            else:\n",
    "                new_label = 'KEINE HATE SPEECH'\n",
    "            assert new_label in ['HATE SPEECH', 'TOXIC SPEECH', 'KEINE HATE SPEECH']\n",
    "        else:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'A'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'C'\n",
    "            else:\n",
    "                new_label = 'B'\n",
    "            assert new_label in ['A', 'B', 'C']\n",
    "\n",
    "    return new_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "02978fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_train_and_eval_sets(data_dir: str, dataset_num: int, task_num: int, sample_size: int, dataset_eval:str) \\\n",
    "        -> dict[str, pd.DataFrame]:\n",
    "    datasets = dict()\n",
    "\n",
    "    train_dataset_task_files = glob.glob(os.path.join(data_dir, f'ds_{dataset_num}__task_{task_num}_train_set*.csv'))\n",
    "    eval_set_name = f'ds_{dataset_num}__task_{task_num}_eval_set'\n",
    "    datasets[eval_set_name] = pd.read_csv(os.path.join(data_dir, eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    # Load the additional evaluation dataset specified by dataset_eval\n",
    "    second_eval_set_name = f'ds_{dataset_eval}__task_{task_num}_full_eval'\n",
    "    datasets[second_eval_set_name] = pd.read_csv(os.path.join(data_dir, second_eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    if sample_size == 'all':\n",
    "        train_dfs_ = {fn.strip('.csv'): pd.read_csv(fn, encoding='utf-8') for fn in train_dataset_task_files}\n",
    "        datasets.update(train_dfs_)\n",
    "    else:\n",
    "        train_df_fn = f'ds_{dataset_num}__task_{task_num}_train_set_{sample_size}'\n",
    "        datasets[train_df_fn] = pd.read_csv(os.path.join(data_dir, train_df_fn + '.csv'), encoding='utf-8')\n",
    "\n",
    "        if train_df_fn not in [os.path.basename(fn).strip('.csv') for fn in train_dataset_task_files]:\n",
    "            raise ValueError(f\"Sample size {sample_size} not found for\"\n",
    "                             f\" dataset {dataset_num} and task {task_num}\")\n",
    "\n",
    "    return datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "904e16c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_training_example(system_prompt, user_prompt_format, user_prompt_text, completion):\n",
    "    return {'messages': [\n",
    "        {'role': 'system',\n",
    "         'content': system_prompt},\n",
    "\n",
    "        {'role': 'user',\n",
    "         'content': user_prompt_format.format(text=user_prompt_text)},\n",
    "\n",
    "        {'role': 'assistant',\n",
    "         'content': completion}\n",
    "    ]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bdf692a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets):\n",
    "    hatespeech_open_ai_metadata = list()\n",
    "\n",
    "    df_id_metadata = pd.DataFrame() if not os.path.exists('hatespeech_open_ai_metadata.csv') \\\n",
    "        else pd.read_csv('hatespeech_open_ai_metadata.csv')\n",
    "\n",
    "    for df_name, df in datasets.items():\n",
    "        df_jsonl_filename = os.path.join(output_dir, 'temp', df_name + '.jsonl')\n",
    "        write_jsonl(data_list=df['openai_instance_format'].tolist(), filename=df_jsonl_filename)\n",
    "\n",
    "        if not_use_full_labels:\n",
    "            df_name += '_single_letter_labels'\n",
    "\n",
    "        if (not rewrite_df_in_openai and\n",
    "                (len(df_id_metadata) > 0 and df_name in df_id_metadata['df_name'].tolist())):\n",
    "            print(f\"Dataset {df_name} already uploaded to OpenAI\")\n",
    "            continue\n",
    "\n",
    "        print(f\"Uploading {df_name} to OpenAI\")\n",
    "        df_response = client.files.create(\n",
    "            file=open(df_jsonl_filename, \"rb\"), purpose=\"fine-tune\"\n",
    "        )\n",
    "        df_response_dict = df_response.to_dict()\n",
    "        df_file_id = df_response_dict[\"id\"]\n",
    "\n",
    "        # Wait until the file is processed\n",
    "        while True:\n",
    "            file = client.files.retrieve(df_file_id)\n",
    "            file_dict = file.to_dict()\n",
    "            if file_dict[\"status\"] == \"processed\":\n",
    "                break\n",
    "            time.sleep(15)\n",
    "        hatespeech_open_ai_metadata.append({'df_name': df_name, 'file_id': df_file_id})\n",
    "\n",
    "    df_id_metadata = pd.concat([df_id_metadata, pd.DataFrame(hatespeech_open_ai_metadata)])\n",
    "    df_id_metadata.to_csv('hatespeech_open_ai_metadata.csv', index=False)\n",
    "\n",
    "    return df_id_metadata\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ace921b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name, n_epochs):\n",
    "    response = client.fine_tuning.jobs.create(\n",
    "        training_file=training_file_id,\n",
    "        validation_file=evaluation_file_id,\n",
    "        model=\"gpt-4o-mini-2024-07-18\",\n",
    "        suffix=model_name,\n",
    "        hyperparameters={\"n_epochs\": n_epochs,\n",
    "                         \"batch_size\": n_batch}\n",
    "    )\n",
    "\n",
    "    response_dict = response.to_dict()\n",
    "    job_id = response_dict[\"id\"]\n",
    "    print(\"Job ID:\", response_dict[\"id\"])\n",
    "    print(\"Status:\", response_dict[\"status\"])\n",
    "\n",
    "    # Wait until the job is done\n",
    "    while True:\n",
    "        job = client.fine_tuning.jobs.retrieve(job_id)\n",
    "        job_dict = job.to_dict()\n",
    "        if job_dict[\"status\"] == \"succeeded\":\n",
    "            break\n",
    "        elif job_dict[\"status\"] == \"failed\":\n",
    "            raise Exception(\"Training failed: %s\" % job_dict[\"error\"])\n",
    "        time.sleep(30)\n",
    "\n",
    "    return job_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2026f811",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_and_log_finetuning_event_history(job_id):\n",
    "    response = client.fine_tuning.jobs.list_events(fine_tuning_job_id=job_id)\n",
    "    response_dict = response.to_dict()\n",
    "    events = response_dict[\"data\"]\n",
    "    events.reverse()\n",
    "    for event in events:\n",
    "        print(event[\"message\"])\n",
    "\n",
    "    # Log events\n",
    "    for event in events:\n",
    "        if event['type'] != 'metrics':\n",
    "            continue\n",
    "        data = event['data']\n",
    "        wandb.log(\n",
    "            {\n",
    "                \"train_loss\": data.get(\"train_loss\"),\n",
    "                \"valid_loss\": data.get(\"valid_loss\"),\n",
    "                \"train_mean_token_accuracy\": data.get(\"train_mean_token_accuracy\"),\n",
    "                \"valid_mean_token_accuracy\": data.get(\"valid_mean_token_accuracy\")\n",
    "            },\n",
    "            step=data.get('step', 0)\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc50a97",
   "metadata": {},
   "source": [
    "# Main Implementation\n",
    "This section contains the main code for fine-tuning the ChatGPT model. It includes setup, data preparation, training, and evaluation steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1b7246",
   "metadata": {},
   "source": [
    "### Connect to WandB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "37564c84",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mkublima\u001b[0m (\u001b[33mdigdemlab\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "wandb version 0.19.7 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.15.11"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\\wandb\\run-20250228_121412-mncf89m4</code>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/mncf89m4' target=\"_blank\">finetune_chetGPT_hatespeech_gpt_zero</a></strong> to <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/mncf89m4' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/mncf89m4</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/mncf89m4?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
      ],
      "text/plain": [
       "<wandb.sdk.wandb_run.Run at 0x1dd701f77c0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 1/2\n",
    "# Initialize the Weights and Biases run\n",
    "wandb.init(\n",
    "    # set the wandb project where this run will be logged\n",
    "    project=WANDB_PROJECT_NAME,\n",
    "    name=run_name if run_name != '' else f'{MODEL_NAME}_ds_{dataset}_task_{int(task)}'\n",
    "                                                    f'_sample_{sample_size}_epochs_{n_epochs}'\n",
    "                                                    f'_full_label_names_{str(not not_use_full_labels)}'\n",
    "                                                    f'_temp_{temp}',\n",
    "\n",
    "    # track hyperparameters and run metadata\n",
    "    config = {\n",
    "        \"model\": MODEL_NAME,\n",
    "        \"dataset\": dataset_num_to_dataset_name[int(dataset)],\n",
    "        \"task\": task_num_to_task_name[int(task)],\n",
    "        \"epochs\": n_epochs,\n",
    "        \"temp\": temp\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f1df25",
   "metadata": {},
   "source": [
    "### Load and Process Data Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2454b3ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 3\n",
    "# Load the dataset and filename\n",
    "dataset_idx, dataset_task_mappings = load_dataset_task_prompt_mappings(\n",
    "    dataset_num=dataset, task_num=task, dataset_task_mappings_fp=dataset_task_mappings_fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d35437fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 5\n",
    "# Load the train and eval datasets\n",
    "datasets = load_train_and_eval_sets(\n",
    "    data_dir=data_dir, dataset_num=dataset, task_num=task, sample_size=sample_size, dataset_eval=dataset_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9f379906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact prompts>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 4\n",
    "# Get information specific to the dataset\n",
    "label_column = dataset_task_mappings.loc[dataset_idx, \"label_column\"]\n",
    "system_prompt = dataset_task_mappings.loc[dataset_idx, 'zero_shot_prompt']\n",
    "user_prompt_format = dataset_task_mappings.loc[dataset_idx, 'user_prompt']\n",
    "    \n",
    "# Log the system prompt and user_prompt_format as files in wandb\n",
    "prompts_artifact = wandb.Artifact('prompts', type='prompts')\n",
    "with prompts_artifact.new_file('system_prompt.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(system_prompt)\n",
    "with prompts_artifact.new_file('user_prompt_format.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(user_prompt_format)\n",
    "wandb.run.log_artifact(prompts_artifact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "47917349",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 6\n",
    "# Generate the training and evaluation examples in the way expected by the Open AI API to finetune chatgpt3.5\n",
    "preprocessed_output_dir = os.path.join(\n",
    "    output_dir, 'preprocessed', 'full_name_labels' if not not_use_full_labels else 'single_letter_labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5a9616d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 7\n",
    "for df_name, df in datasets.items():\n",
    "    df['completion_label'] = df[label_column].map(\n",
    "        lambda label: map_label_to_completion(label=label, task_num = task,\n",
    "                                              full_label=not not_use_full_labels)\n",
    "        )\n",
    "    df['openai_instance_format'] = df.apply(\n",
    "        lambda row: create_training_example(\n",
    "            system_prompt=system_prompt, user_prompt_format=user_prompt_format,\n",
    "            user_prompt_text=row['text'],\n",
    "            completion=row['completion_label']\n",
    "        ),\n",
    "        axis=1\n",
    "    )\n",
    "    df['openai_instance_without_completion'] = df['openai_instance_format'].map(lambda x: x['messages'][:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8bcd8c12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check for errors ds_7__task_1_train_set_100 set: \n",
      "No errors found\n"
     ]
    }
   ],
   "source": [
    "# Section 8\n",
    "print(f'Check for errors {df_name} set: ')\n",
    "assert not dataset_has_format_errors(df['openai_instance_format'].tolist()), f\"Errors found in {df_name}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9b1d7833",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 9\n",
    "df.to_csv(os.path.join(preprocessed_output_dir, df_name + '.csv'), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24cdc6b4",
   "metadata": {},
   "source": [
    "### Upload Training Dataset to OPEN AI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2fd9b187",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading ds_7__task_1_eval_set to OpenAI\n",
      "Uploading ds_x__task_1_full_eval to OpenAI\n",
      "Uploading ds_7__task_1_train_set_100 to OpenAI\n"
     ]
    }
   ],
   "source": [
    "# Section 10\n",
    "# Create jsonl file and upload to OpenAI\n",
    "df_id_metadata =upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "705758da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 11\n",
    "# delete all files in the temp folder\n",
    "os.system(f\"rm -rf {os.path.join(output_dir, 'temp')}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4519ab5a",
   "metadata": {},
   "source": [
    "### Finetune chatGPT with the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b0ec6e82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 12\n",
    "# Run training on the train_df samples selected\n",
    "eval_set_name = f'ds_{dataset}__task_{task}_eval_set'\n",
    "eval_df = datasets[eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a76b1ab7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finetuning ds_7__task_1_train_set_100\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Section 13\n",
    "print(f\"Finetuning {df_name}\")\n",
    "print('-' * 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a1036bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 14\n",
    "# Log in wandb.config the dataset sample size used\n",
    "sample_size = df_name.split('_')[-1]\n",
    "wandb.config['trainset_size'] = sample_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d0860e65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the evaluation set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 847, 997\n",
      "mean / median: 879.4873096446701, 869.0\n",
      "p5 / p95: 853.0, 920.0\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 4.8908629441624365, 5.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~346518 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~1732590 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 15\n",
    "# Check statistics and possible cost for each dataset\n",
    "print('Statistics for the evaluation set: ')\n",
    "eval_tokens = check_token_statistics_and_cost_estimate(\n",
    "    datasets[eval_set_name]['openai_instance_format'].tolist(), target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "254bdfe1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the training set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 848, 997\n",
      "mean / median: 878.96, 867.0\n",
      "p5 / p95: 853.0, 918.0\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 4.72, 4.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~87896 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~439480 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 16\n",
    "print(f'Statistics for the training set: ')\n",
    "train_tokens = check_token_statistics_and_cost_estimate(df['openai_instance_format'].tolist(),\n",
    "                                                        target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "91aed7d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of tokens: 434414\n",
      "\n",
      "#### Estimated cost: 3.48 USD\n"
     ]
    }
   ],
   "source": [
    "# Section 17\n",
    "print(f\"Total number of tokens: {eval_tokens + train_tokens}\\n\")\n",
    "print(f\"#### Estimated cost: {round((eval_tokens + train_tokens) * (0.008 / 1000), 2)} USD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5a4cb776",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 18\n",
    "# Run the fine-tuning job\n",
    "training_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == df_name, 'file_id'].values[0]\n",
    "evaluation_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == eval_set_name, 'file_id'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6b0baf56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job ID: ftjob-5Iz8zjkbUgKpiBoxSY8x3gFF\n",
      "Status: validating_files\n"
     ]
    }
   ],
   "source": [
    "# Section 19\n",
    "model_name = (df_name.replace('__', '_')\n",
    "              .replace('train_set', 'trn')\n",
    "              .replace('task', 't')\n",
    "              .replace('_single_letter_labels', '_sl'))\n",
    "job_id = fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name=model_name, n_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6aff8caf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model ft:gpt-4o-mini-2024-07-18:university-of-zurich:ds-7-t-1-trn-100:B5slhSVm has been successfully fine-tuned\n"
     ]
    }
   ],
   "source": [
    "# Section 20\n",
    "# Print the model name\n",
    "response = client.fine_tuning.jobs.retrieve(job_id)\n",
    "response_dict = response.to_dict()\n",
    "full_model_name = response_dict[\"fine_tuned_model\"]\n",
    "print(f'The model {full_model_name} has been successfully fine-tuned')\n",
    "wandb.config['model_name_openai'] = full_model_name\n",
    "wandb.config['finetuning_jobid'] = job_id\n",
    "wandb.config['training_file_openai_id'] = response_dict['training_file']\n",
    "wandb.config['validation_file_openai_id'] = response_dict['validation_file']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "45b169bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 10/25: training loss=0.07, validation loss=0.23, full validation loss=0.13\n",
      "Step 11/25: training loss=0.07, validation loss=0.06\n",
      "Step 12/25: training loss=0.07, validation loss=0.06\n",
      "Step 13/25: training loss=0.04, validation loss=0.07\n",
      "Step 14/25: training loss=0.02, validation loss=0.12\n",
      "Step 15/25: training loss=0.03, validation loss=0.11, full validation loss=0.10\n",
      "Step 16/25: training loss=0.01, validation loss=0.14\n",
      "Step 17/25: training loss=0.06, validation loss=0.05\n",
      "Step 18/25: training loss=0.00, validation loss=0.06\n",
      "Step 19/25: training loss=0.01, validation loss=0.06\n",
      "Step 20/25: training loss=0.00, validation loss=0.05, full validation loss=0.08\n",
      "Step 21/25: training loss=0.00, validation loss=0.08\n",
      "Step 22/25: training loss=0.00, validation loss=0.12\n",
      "Step 23/25: training loss=0.00, validation loss=0.11\n",
      "Step 24/25: training loss=0.01, validation loss=0.22\n",
      "Step 25/25: training loss=0.03, validation loss=0.05, full validation loss=0.10\n",
      "Checkpoint created at step 15\n",
      "Checkpoint created at step 20\n",
      "New fine-tuned model created\n",
      "The job has successfully completed\n"
     ]
    }
   ],
   "source": [
    "# Section 21\n",
    "# Print the events (training history of the model)\n",
    "print_and_log_finetuning_event_history(job_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c579b37",
   "metadata": {},
   "source": [
    "## Evaluate Model on Validation Set with Annotations of the Group from which the Training Data is!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fba537b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Section 22\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0a544a5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 394/394 [03:25<00:00,  1.92it/s]\n"
     ]
    }
   ],
   "source": [
    "# Section 23\n",
    "for messages in tqdm(eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "337b13ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 24\n",
    "# Add predictions to df\n",
    "eval_df['prediction'] = predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fe242636",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 25\n",
    "# Store output\n",
    "predictions_output_dir = os.path.join(output_dir, 'predictions',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir, exist_ok=True)\n",
    "datasets[eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bcaddde5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 26\n",
    "# Get performance metrics\n",
    "y_true = eval_df['completion_label']\n",
    "y_pred = eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3d085eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 27\n",
    "label_type = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels = task_to_display_labels[task][label_type]\n",
    "labels = display_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a1ce1492",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.855421686746988,\n",
      "                 'precision': 0.9403973509933775,\n",
      "                 'recall': 0.7845303867403315,\n",
      "                 'support': 181},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.9103448275862069,\n",
      "                       'precision': 0.868421052631579,\n",
      "                       'recall': 0.9565217391304348,\n",
      "                       'support': 138},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.7349397590361445,\n",
      "                  'precision': 0.6703296703296703,\n",
      "                  'recall': 0.8133333333333334,\n",
      "                  'support': 75},\n",
      " 'accuracy': 0.850253807106599,\n",
      " 'macro avg': {'f1-score': 0.8335687577897798,\n",
      "               'precision': 0.826382691318209,\n",
      "               'recall': 0.8514618197346998,\n",
      "               'support': 394},\n",
      " 'weighted avg': {'f1-score': 0.8517243488218584,\n",
      "                  'precision': 0.8637785560093515,\n",
      "                  'recall': 0.850253807106599,\n",
      "                  'support': 394}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Section 28\n",
    "cm_plot, classification_report, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true, y_pred, display_labels, labels, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "75c64dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 29\n",
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4aaa4209",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 31\n",
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "45aaca05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_ds_7_t_1_trn_100>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 32\n",
    "# Log the classification report as an artifact\n",
    "classification_report = (pd.DataFrame({k: v for k, v in classification_report.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report': wandb.Table(\n",
    "    dataframe=classification_report)})\n",
    "\n",
    "classification_report_artifact = wandb.Artifact(\n",
    "      f'classification_report_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact.new_file('classification_report.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6db7bbf2",
   "metadata": {},
   "source": [
    "## Evaluation on Second Evaluation Set\n",
    "In this section, we will evaluate the performance of our model on a second evaluation set (`second_eval_set`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8cbb7ff7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['ds_7__task_1_eval_set', 'ds_x__task_1_full_eval', 'ds_7__task_1_train_set_100'])\n"
     ]
    }
   ],
   "source": [
    "print(datasets.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "second_eval_set_name = f'ds_{dataset_eval}__task_{task}_full_eval'\n",
    "full_eval_df = datasets[second_eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == second_eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9cfc903c",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_eval_df = datasets[second_eval_set_name]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "303aeed5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Load the second evaluation set (second_eval_set)\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions_2 = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7c218926",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 494/494 [04:37<00:00,  1.78it/s]\n"
     ]
    }
   ],
   "source": [
    "for messages in tqdm(full_eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions_2.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "789fce14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add predictions to df\n",
    "full_eval_df['prediction'] = predictions_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "616c4658",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_output_dir_2 = os.path.join(output_dir, 'predictions_2',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir_2, exist_ok=True)\n",
    "datasets[second_eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir_2, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ec8481ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get performance metrics\n",
    "y_true_2 = full_eval_df['completion_label']\n",
    "y_pred_2 = full_eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "296deb14",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_type_2 = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels_2 = task_to_display_labels[task][label_type]\n",
    "labels_2 = display_labels_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "83dbd847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.6628242074927954,\n",
      "                 'precision': 0.5665024630541872,\n",
      "                 'recall': 0.7986111111111112,\n",
      "                 'support': 144},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.751152073732719,\n",
      "                       'precision': 0.9157303370786517,\n",
      "                       'recall': 0.63671875,\n",
      "                       'support': 256},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.5507246376811595,\n",
      "                  'precision': 0.504424778761062,\n",
      "                  'recall': 0.6063829787234043,\n",
      "                  'support': 94},\n",
      " 'accuracy': 0.6781376518218624,\n",
      " 'macro avg': {'f1-score': 0.6549003063022246,\n",
      "               'precision': 0.6622191929646336,\n",
      "               'recall': 0.6805709466115051,\n",
      "               'support': 494},\n",
      " 'weighted avg': {'f1-score': 0.6872666653776672,\n",
      "                  'precision': 0.7356664983309263,\n",
      "                  'recall': 0.6781376518218624,\n",
      "                  'support': 494}}\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
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     "metadata": {},
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   ],
   "source": [
    "cm_plot, classification_report_2, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true_2, y_pred_2, display_labels_2, labels_2, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "20289ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "09a5abfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix_2': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2399e29a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_2_ds_7_t_1_trn_100>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Log the classification report as an artifact\n",
    "classification_report_2 = (pd.DataFrame({k: v for k, v in classification_report_2.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report_2': wandb.Table(\n",
    "    dataframe=classification_report_2)})\n",
    "\n",
    "classification_report_artifact_2 = wandb.Artifact(\n",
    "      f'classification_report_2_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact_2.new_file('classification_report_2.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report_2))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b0aa2d8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
      ],
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       "<IPython.core.display.HTML object>"
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       "<style>\n",
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       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>█▁</td></tr><tr><td>f1</td><td>█▁</td></tr><tr><td>precision</td><td>█▁</td></tr><tr><td>recall</td><td>█▁</td></tr><tr><td>train_loss</td><td>███▅▃▄▂▇▁▂▁▁▁▁▂▄</td></tr><tr><td>train_mean_token_accuracy</td><td>▅▄▁▆▆▅▆▃█▆████▆▆</td></tr><tr><td>valid_loss</td><td>█▁▁▂▄▃▄▁▁▁▁▂▄▃█▁</td></tr><tr><td>valid_mean_token_accuracy</td><td>▁▇▅█▅▆▅▇▇▆▇▆▇▆▅█</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>0.67814</td></tr><tr><td>f1</td><td>0.67814</td></tr><tr><td>precision</td><td>0.67814</td></tr><tr><td>recall</td><td>0.67814</td></tr><tr><td>train_loss</td><td>0.02586</td></tr><tr><td>train_mean_token_accuracy</td><td>0.9927</td></tr><tr><td>valid_loss</td><td>0.05252</td></tr><tr><td>valid_mean_token_accuracy</td><td>0.98561</td></tr></table><br/></div></div>"
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    {
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       " View run <strong style=\"color:#cdcd00\">finetune_chetGPT_hatespeech_gpt_zero</strong> at: <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/mncf89m4' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/mncf89m4</a><br/>Synced 6 W&B file(s), 4 media file(s), 4 artifact file(s) and 0 other file(s)"
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       "Find logs at: <code>.\\wandb\\run-20250228_121412-mncf89m4\\logs</code>"
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    "# Mark the end of the run\n",
    "wandb.finish()"
   ]
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